Improved software self-adaptive test method

Abstract

The invention relates to an improved software self-adaptive test method. The method is based on a software control theory model, is improved for a traditional self-adaptive random test method, takes tested input domain information as test historical data, adjusts an algorithm as a parameter estimation module, selects partitions and input domains as test decisions, estimates the number of test cases in the input domains to serve as an input domain test step length design, and realizes the purpose of improving the test efficiency by reasonably allocating test resources. According to the key problems faced by software testing, the traditional software self-adaptive testing method is improved, the problem of software input space combination explosion can be effectively solved, intermediate testing data generated in the testing process are fully utilized, dynamic and self-adaptive feedback adjustment of the software testing process is achieved, the number of test cases can be effectively reduced, the test efficiency is improved, and the test cost is reduced. Therefore, the method has important engineering application value.

Description

technical field [0001] The invention belongs to the technical field of software engineering and software testing, and in particular relates to an improved software self-adaptive testing method. Background technique [0002] With the rapid development of information technology and the increasing complexity and scale of software systems, software testing, as an important part of the life cycle, plays an increasingly important role. However, engineering practice shows that the difficulty and cost of software testing become increasingly uncontrollable with the increasing complexity and scale of software. Traditional software testing methods are obviously insufficient in testing efficiency and fault location, and can no longer meet the high standards and high requirements of modern software testing. [0003] The traditional adaptive software testing is based on the software cybernetics model, including the software under test (controlled object), test strategy (controller) and p...

AI Technical Summary

Problems solved by technology

[0004] 1) The model is difficult to fundamentally solve the problem of geometric increase in test time overhead

[0005] 2) Algorithm efficiency problem in the case of multi-dimensional, in the case of more than three dimensions, the computational efficiency of adaptive software testing is not good, and consumes a lot of testing overhead

The time complexity of adaptive software testing in screening a test case reaches O(n2), and the overhead of the algorithm for screening test cases affects the operating efficiency of the algorithm

[0006] 3) Under the condition of non-numerical input fields, there are great difficulties in the representation and calculation of the distance between use cases

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